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  <section id="torch-tensorrt">
<span id="torch-tensorrt-py"></span><h1>torch_tensorrt<a class="headerlink" href="#torch-tensorrt" title="Permalink to this headline">¶</a></h1>
<span class="target" id="module-torch_tensorrt"></span><section id="functions">
<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="torch_tensorrt.set_device">
<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gpu_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_util.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.set_device" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch_tensorrt.compile">
<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">typing.Any</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ir='default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={&lt;dtype.float:</span> <span class="pre">0&gt;}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_compile.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.compile" title="Permalink to this definition">¶</a></dt>
<dd><p>Compile a PyTorch module for NVIDIA GPUs using TensorRT</p>
<p>Takes a existing PyTorch module and a set of settings to configure the compiler
and using the path specified in <code class="docutils literal notranslate"><span class="pre">ir</span></code> lower and compile the module to TensorRT
returning a PyTorch Module back</p>
<p>Converts specifically the forward method of a Module</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>module</strong> (<em>Union</em><em>(</em><em>torch.nn.Module</em><em>,</em><em>torch.jit.ScriptModule</em>) – Source module</p>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
to select device type.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
    torch_tensorrt.Input(
        min_shape=(1, 224, 224, 3),
        opt_shape=(1, 512, 512, 3),
        max_shape=(1, 1024, 1024, 3),
        dtype=torch.int32
        format=torch.channel_last
    ), # Dynamic input shape for input #2
    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
]
</pre></div>
</div>
</p></li>
<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
<li><p><strong>ir</strong> (<em>str</em>) – The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)</p></li>
<li><p><strong>**kwargs</strong> – Additional settings for the specific requested strategy (See submodules for more info)</p></li>
</ul>
</dd>
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Compiled Module, when run it will execute via TensorRT</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>torch.nn.Module</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch_tensorrt.convert_method_to_trt_engine">
<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">convert_method_to_trt_engine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">typing.Any</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name:</span> <span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ir='default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={&lt;dtype.float:</span> <span class="pre">0&gt;}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_compile.html#convert_method_to_trt_engine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.convert_method_to_trt_engine" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert a TorchScript module method to a serialized TensorRT engine</p>
<p>Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>module</strong> (<em>Union</em><em>(</em><em>torch.nn.Module</em><em>,</em><em>torch.jit.ScriptModule</em>) – Source module</p>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
to select device type.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
    torch_tensorrt.Input(
        min_shape=(1, 224, 224, 3),
        opt_shape=(1, 512, 512, 3),
        max_shape=(1, 1024, 1024, 3),
        dtype=torch.int32
        format=torch.channel_last
    ), # Dynamic input shape for input #2
    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
]
</pre></div>
</div>
</p></li>
<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
<li><p><strong>ir</strong> (<em>str</em>) – The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)</p></li>
<li><p><strong>**kwargs</strong> – Additional settings for the specific requested strategy (See submodules for more info)</p></li>
</ul>
</dd>
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>bytes</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch_tensorrt.get_build_info">
<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">get_build_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/_util.html#get_build_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.get_build_info" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a string containing the build information of torch_tensorrt distribution</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>String containing the build information for torch_tensorrt distribution</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>str</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch_tensorrt.dump_build_info">
<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">dump_build_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_util.html#dump_build_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dump_build_info" title="Permalink to this definition">¶</a></dt>
<dd><p>Prints build information about the torch_tensorrt distribution to stdout</p>
</dd></dl>

</section>
<section id="classes">
<h2>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="torch_tensorrt.Input">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">Input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Input.html#Input"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Input" title="Permalink to this definition">¶</a></dt>
<dd><p>Defines an input to a module in terms of expected shape, data type and tensor format.</p>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>shape_mode</strong> (<em>torch_tensorrt.Input._ShapeMode</em>) – Is input statically or dynamically shaped</p></li>
<li><p><strong>shape</strong> (<em>Tuple</em><em> or </em><em>Dict</em>) – <p>Either a single Tuple or a dict of tuples defining the input shape.
Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form
<a href="#id1"><span class="problematic" id="id2">``</span></a>{</p>
<blockquote>
<div><p>”min_shape”: Tuple,
“opt_shape”: Tuple,
“max_shape”: Tuple</p>
</div></blockquote>
<p>}``</p>
</p></li>
<li><p><strong>dtype</strong> (<em>torch_tensorrt.dpython:type</em>) – The expected data type of the input tensor (default: torch_tensorrt.dtype.float32)</p></li>
<li><p><strong>format</strong> (<a class="reference internal" href="#torch_tensorrt.TensorFormat" title="torch_tensorrt.TensorFormat"><em>torch_tensorrt.TensorFormat</em></a>) – The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch_tensorrt.Input.__init__">
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Input.html#Input.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Input.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>__init__ Method for torch_tensorrt.Input</p>
<p>Input accepts one of a few construction patterns</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Static shape of input tensor</p>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Static shape of input tensor</p></li>
<li><p><strong>min_shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Min size of input tensor’s shape range
Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input’s shape_mode to DYNAMIC</p></li>
<li><p><strong>opt_shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Opt size of input tensor’s shape range
Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input’s shape_mode to DYNAMIC</p></li>
<li><p><strong>max_shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Max size of input tensor’s shape range
Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input’s shape_mode to DYNAMIC</p></li>
<li><p><strong>dtype</strong> (<em>torch.dpython:type</em><em> or </em><em>torch_tensorrt.dpython:type</em>) – Expected data type for input tensor (default: torch_tensorrt.dtype.float32)</p></li>
<li><p><strong>format</strong> (<em>torch.memory_format</em><em> or </em><a class="reference internal" href="#torch_tensorrt.TensorFormat" title="torch_tensorrt.TensorFormat"><em>torch_tensorrt.TensorFormat</em></a>) – The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Examples</p>
<ul class="simple">
<li><p>Input([1,3,32,32], dtype=torch.float32, format=torch.channel_last)</p></li>
<li><p>Input(shape=(1,3,32,32), dtype=torch_tensorrt.dtype.int32, format=torch_tensorrt.TensorFormat.NCHW)</p></li>
<li><p>Input(min_shape=(1,3,32,32), opt_shape=[2,3,32,32], max_shape=(3,3,32,32)) #Implicitly dtype=torch_tensorrt.dtype.float32, format=torch_tensorrt.TensorFormat.NCHW</p></li>
</ul>
</dd></dl>

<dl class="py attribute">
<dt class="sig sig-object py" id="torch_tensorrt.Input.dtype">
<span class="sig-name descname"><span class="pre">dtype</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;dtype.unknown:</span> <span class="pre">5&gt;</span></em><a class="headerlink" href="#torch_tensorrt.Input.dtype" title="Permalink to this definition">¶</a></dt>
<dd><p>torch_tensorrt.dtype.float32)</p>
<dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>The expected data type of the input tensor (default</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="torch_tensorrt.Input.example_tensor">
<span class="sig-name descname"><span class="pre">example_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimization_profile_field</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/_Input.html#Input.example_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Input.example_tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Get an example tensor of the shape specified by the Input object</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>optimization_profile_field</strong> (<em>Optional</em><em>(</em><em>str</em><em>)</em>) – Name of the field to use for shape in the case the Input is dynamically shaped</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A PyTorch Tensor</p>
</dd>
</dl>
</dd></dl>

<dl class="py attribute">
<dt class="sig sig-object py" id="torch_tensorrt.Input.format">
<span class="sig-name descname"><span class="pre">format</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;TensorFormat.contiguous:</span> <span class="pre">0&gt;</span></em><a class="headerlink" href="#torch_tensorrt.Input.format" title="Permalink to this definition">¶</a></dt>
<dd><p>torch_tensorrt.TensorFormat.NCHW)</p>
<dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>The expected format of the input tensor (default</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="torch_tensorrt.Input.from_tensor">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#torch_tensorrt.Input" title="torch_tensorrt._Input.Input"><span class="pre">torch_tensorrt._Input.Input</span></a></span></span><a class="reference internal" href="../_modules/torch_tensorrt/_Input.html#Input.from_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Input.from_tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Produce a Input which contains the information of the given PyTorch tensor.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>tensor</strong> (<em>torch.Tensor</em>) – A PyTorch tensor.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A Input object.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="torch_tensorrt.Input.from_tensors">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_tensors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch_tensorrt.Input" title="torch_tensorrt._Input.Input"><span class="pre">torch_tensorrt._Input.Input</span></a><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/_Input.html#Input.from_tensors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Input.from_tensors" title="Permalink to this definition">¶</a></dt>
<dd><p>Produce a list of Inputs which contain
the information of all the given PyTorch tensors.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>tensors</strong> (<em>Iterable</em><em>[</em><em>torch.Tensor</em><em>]</em>) – A list of PyTorch tensors.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A list of Inputs.</p>
</dd>
</dl>
</dd></dl>

<dl class="py attribute">
<dt class="sig sig-object py" id="torch_tensorrt.Input.shape">
<span class="sig-name descname"><span class="pre">shape</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#torch_tensorrt.Input.shape" title="Permalink to this definition">¶</a></dt>
<dd><p>Either a single Tuple or a dict of tuples defining the input shape. Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form <code class="docutils literal notranslate"><span class="pre">{</span> <span class="pre">&quot;min_shape&quot;:</span> <span class="pre">Tuple,</span> <span class="pre">&quot;opt_shape&quot;:</span> <span class="pre">Tuple,</span> <span class="pre">&quot;max_shape&quot;:</span> <span class="pre">Tuple</span> <span class="pre">}</span></code></p>
<dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>(Tuple or Dict)</p>
</dd>
</dl>
</dd></dl>

<dl class="py attribute">
<dt class="sig sig-object py" id="torch_tensorrt.Input.shape_mode">
<span class="sig-name descname"><span class="pre">shape_mode</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#torch_tensorrt.Input.shape_mode" title="Permalink to this definition">¶</a></dt>
<dd><p>Is input statically or dynamically shaped</p>
<dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>(torch_tensorrt.Input._ShapeMode)</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="torch_tensorrt.Device">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">Device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Device.html#Device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Device" title="Permalink to this definition">¶</a></dt>
<dd><p>Defines a device that can be used to specify target devices for engines</p>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>device_type</strong> (<a class="reference internal" href="#torch_tensorrt.DeviceType" title="torch_tensorrt.DeviceType"><em>torch_tensorrt.DeviceType</em></a>) – Target device type (GPU or DLA). Set implicitly based on if dla_core is specified.</p></li>
<li><p><strong>gpu_id</strong> (<em>python:int</em>) – Device ID for target GPU</p></li>
<li><p><strong>dla_core</strong> (<em>python:int</em>) – Core ID for target DLA core</p></li>
<li><p><strong>allow_gpu_fallback</strong> (<em>bool</em>) – Whether falling back to GPU if DLA cannot support an op should be allowed</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch_tensorrt.Device.__init__">
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Device.html#Device.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Device.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>__init__ Method for torch_tensorrt.Device</p>
<p>Device accepts one of a few construction patterns</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>spec</strong> (<em>str</em>) – String with device spec e.g. “dla:0” for dla, core_id 0</p>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>gpu_id</strong> (<em>python:int</em>) – ID of target GPU (will get overrided if dla_core is specified to the GPU managing DLA). If specified, no positional arguments should be provided</p></li>
<li><p><strong>dla_core</strong> (<em>python:int</em>) – ID of target DLA core. If specified, no positional arguments should be provided.</p></li>
<li><p><strong>allow_gpu_fallback</strong> (<em>bool</em>) – Allow TensorRT to schedule operations on GPU if they are not supported on DLA (ignored if device type is not DLA)</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Examples</p>
<ul class="simple">
<li><p>Device(“gpu:1”)</p></li>
<li><p>Device(“cuda:1”)</p></li>
<li><p>Device(“dla:0”, allow_gpu_fallback=True)</p></li>
<li><p>Device(gpu_id=0, dla_core=0, allow_gpu_fallback=True)</p></li>
<li><p>Device(dla_core=0, allow_gpu_fallback=True)</p></li>
<li><p>Device(gpu_id=1)</p></li>
</ul>
</dd></dl>

<dl class="py attribute">
<dt class="sig sig-object py" id="torch_tensorrt.Device.allow_gpu_fallback">
<span class="sig-name descname"><span class="pre">allow_gpu_fallback</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">False</span></em><a class="headerlink" href="#torch_tensorrt.Device.allow_gpu_fallback" title="Permalink to this definition">¶</a></dt>
<dd><p>(bool) Whether falling back to GPU if DLA cannot support an op should be allowed</p>
</dd></dl>

<dl class="py attribute">
<dt class="sig sig-object py" id="torch_tensorrt.Device.device_type">
<span class="sig-name descname"><span class="pre">device_type</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#torch_tensorrt.Device.device_type" title="Permalink to this definition">¶</a></dt>
<dd><p>Target device type (GPU or DLA). Set implicitly based on if dla_core is specified.</p>
<dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>(<a class="reference internal" href="#torch_tensorrt.DeviceType" title="torch_tensorrt.DeviceType">torch_tensorrt.DeviceType</a>)</p>
</dd>
</dl>
</dd></dl>

<dl class="py attribute">
<dt class="sig sig-object py" id="torch_tensorrt.Device.dla_core">
<span class="sig-name descname"><span class="pre">dla_core</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">-1</span></em><a class="headerlink" href="#torch_tensorrt.Device.dla_core" title="Permalink to this definition">¶</a></dt>
<dd><p>(int) Core ID for target DLA core</p>
</dd></dl>

<dl class="py attribute">
<dt class="sig sig-object py" id="torch_tensorrt.Device.gpu_id">
<span class="sig-name descname"><span class="pre">gpu_id</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">-1</span></em><a class="headerlink" href="#torch_tensorrt.Device.gpu_id" title="Permalink to this definition">¶</a></dt>
<dd><p>(int) Device ID for target GPU</p>
</dd></dl>

</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="torch_tensorrt.TRTModuleNext">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">TRTModuleNext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">serialized_engine:</span> <span class="pre">bytearray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name:</span> <span class="pre">str</span> <span class="pre">=</span> <span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_binding_names:</span> <span class="pre">typing.List[str]</span> <span class="pre">=</span> <span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_binding_names:</span> <span class="pre">typing.List[str]</span> <span class="pre">=</span> <span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_device:</span> <span class="pre">torch_tensorrt._Device.Device</span> <span class="pre">=</span> <span class="pre">&lt;torch_tensorrt._Device.Device</span> <span class="pre">object&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_TRTModuleNext.html#TRTModuleNext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.TRTModuleNext" title="Permalink to this definition">¶</a></dt>
<dd><p>TRTModuleNext is a PyTorch module which encompasses an arbitrary TensorRT Engine.</p>
<p>This module is backed by the Torch-TensorRT runtime and is fully compatibile with both
FX / Python deployments (just <code class="docutils literal notranslate"><span class="pre">import</span> <span class="pre">torch_tensorrt</span></code> as part of the application) as
well as TorchScript / C++ deployments since TRTModule can be passed to <code class="docutils literal notranslate"><span class="pre">torch.jit.trace</span></code>
and then saved.</p>
<p>The forward function is simpily forward(<a href="#id3"><span class="problematic" id="id4">*</span></a>args: torch.Tensor) -&gt; Tuple[torch.Tensor] where
the internal implementation is <code class="docutils literal notranslate"><span class="pre">return</span> <span class="pre">Tuple(torch.ops.tensorrt.execute_engine(list(inputs),</span> <span class="pre">self.engine))</span></code></p>
<p>&gt; Note: TRTModuleNext only supports engines built with explict batch</p>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>name</strong> (<em>str</em>) – Name of module (for easier debugging)</p></li>
<li><p><strong>engine</strong> (<em>torch.classess.tensorrt.Engine</em>) – Torch-TensorRT TensorRT Engine instance, manages [de]serialization, device configuration, profiling</p></li>
<li><p><strong>input_binding_names</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of input TensorRT engine binding names in the order they would be passed to the TRT modules</p></li>
<li><p><strong>output_binding_names</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of output TensorRT engine binding names in the order they should be returned</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch_tensorrt.TRTModuleNext.__init__">
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">serialized_engine:</span> <span class="pre">bytearray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name:</span> <span class="pre">str</span> <span class="pre">=</span> <span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_binding_names:</span> <span class="pre">typing.List[str]</span> <span class="pre">=</span> <span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_binding_names:</span> <span class="pre">typing.List[str]</span> <span class="pre">=</span> <span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_device:</span> <span class="pre">torch_tensorrt._Device.Device</span> <span class="pre">=</span> <span class="pre">&lt;torch_tensorrt._Device.Device</span> <span class="pre">object&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_TRTModuleNext.html#TRTModuleNext.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.TRTModuleNext.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>__init__ method for torch_tensorrt.TRTModuleNext</p>
<p>Takes a name, target device, serialized TensorRT engine, and binding names / order and constructs
a PyTorch <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code> around it.</p>
<p>If binding names are not provided, it is assumed that the engine binding names follow the following convention:</p>
<blockquote>
<div><ul class="simple">
<li><dl class="simple">
<dt>[symbol].[index in input / output array]</dt><dd><ul>
<li><p>ex. [x.0, x.1, x.2] -&gt; [y.0]</p></li>
</ul>
</dd>
</dl>
</li>
</ul>
</div></blockquote>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>name</strong> (<em>str</em>) – Name for module</p></li>
<li><p><strong>serialized_engine</strong> (<em>bytearray</em>) – Serialized TensorRT engine in the form of a bytearray</p></li>
<li><p><strong>input_binding_names</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of input TensorRT engine binding names in the order they would be passed to the TRT modules</p></li>
<li><p><strong>output_binding_names</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of output TensorRT engine binding names in the order they should be returned</p></li>
<li><p><strong>target_device</strong> – (torch_tensorrt.Device): Device to instantiate TensorRT engine on. Must be a compatible device i.e. same GPU model / compute capability as was used to build the engine</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Example</p>
<p>..code-block:: py</p>
<blockquote>
<div><dl class="simple">
<dt>with io.BytesIO() as engine_bytes:</dt><dd><p>engine_bytes.write(trt_engine.serialize())
engine_str = engine_bytes.getvalue()</p>
</dd>
<dt>trt_module = TRTModule(</dt><dd><p>engine_str,
engine_name=”my_module”,
input_names=[“x”],
output_names=[“output”],</p>
</dd>
</dl>
<p>)</p>
</div></blockquote>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="torch_tensorrt.TRTModuleNext.disable_profiling">
<span class="sig-name descname"><span class="pre">disable_profiling</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_TRTModuleNext.html#TRTModuleNext.disable_profiling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.TRTModuleNext.disable_profiling" title="Permalink to this definition">¶</a></dt>
<dd><p>Disable the profiler</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="torch_tensorrt.TRTModuleNext.dump_layer_info">
<span class="sig-name descname"><span class="pre">dump_layer_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_TRTModuleNext.html#TRTModuleNext.dump_layer_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.TRTModuleNext.dump_layer_info" title="Permalink to this definition">¶</a></dt>
<dd><p>Dump layer information encoded by the TensorRT engine in this module to STDOUT</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="torch_tensorrt.TRTModuleNext.enable_profiling">
<span class="sig-name descname"><span class="pre">enable_profiling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">profiling_results_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_TRTModuleNext.html#TRTModuleNext.enable_profiling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.TRTModuleNext.enable_profiling" title="Permalink to this definition">¶</a></dt>
<dd><p>Enable the profiler to collect latency information about the execution of the engine</p>
<p>Traces can be visualized using <a class="reference external" href="https://ui.perfetto.dev/">https://ui.perfetto.dev/</a> or compatible alternatives</p>
<dl class="field-list simple">
<dt class="field-odd">Keyword Arguments</dt>
<dd class="field-odd"><p><strong>profiling_results_dir</strong> (<em>str</em>) – Absolute path to the directory to sort results of profiling.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="torch_tensorrt.TRTModuleNext.forward">
<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_TRTModuleNext.html#TRTModuleNext.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.TRTModuleNext.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Implementation of the forward pass for a TensorRT engine</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>*inputs</strong> (<em>torch.Tensor</em>) – Inputs to the forward function, must all be <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code></p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Result of the engine computation</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>torch.Tensor or Tuple(torch.Tensor)</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="torch_tensorrt.TRTModuleNext.get_extra_state">
<span class="sig-name descname"><span class="pre">get_extra_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_TRTModuleNext.html#TRTModuleNext.get_extra_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.TRTModuleNext.get_extra_state" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns any extra state to include in the module’s state_dict.
Implement this and a corresponding <code class="xref cpp cpp-func docutils literal notranslate"><span class="pre">set_extra_state()</span></code> for your module
if you need to store extra state. This function is called when building the
module’s <cite>state_dict()</cite>.</p>
<p>Note that extra state should be pickleable to ensure working serialization
of the state_dict. We only provide provide backwards compatibility guarantees
for serializing Tensors; other objects may break backwards compatibility if
their serialized pickled form changes.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Any extra state to store in the module’s state_dict</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>object</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="torch_tensorrt.TRTModuleNext.get_layer_info">
<span class="sig-name descname"><span class="pre">get_layer_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/_TRTModuleNext.html#TRTModuleNext.get_layer_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.TRTModuleNext.get_layer_info" title="Permalink to this definition">¶</a></dt>
<dd><p>Get a JSON string containing the layer information encoded by the TensorRT engine in this module</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A JSON string which contains the layer information of the engine incapsulated in this module</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>str</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="torch_tensorrt.TRTModuleNext.set_extra_state">
<span class="sig-name descname"><span class="pre">set_extra_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_TRTModuleNext.html#TRTModuleNext.set_extra_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.TRTModuleNext.set_extra_state" title="Permalink to this definition">¶</a></dt>
<dd><p>This function is called from <code class="xref cpp cpp-func docutils literal notranslate"><span class="pre">load_state_dict()</span></code> to handle any extra state
found within the <cite>state_dict</cite>. Implement this function and a corresponding
<code class="xref cpp cpp-func docutils literal notranslate"><span class="pre">get_extra_state()</span></code> for your module if you need to store extra state within its
<cite>state_dict</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>state</strong> (<em>dict</em>) – Extra state from the <cite>state_dict</cite></p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
<section id="enums">
<h2>Enums<a class="headerlink" href="#enums" title="Permalink to this headline">¶</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="torch_tensorrt.dtype">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">dtype</span></span><a class="headerlink" href="#torch_tensorrt.dtype" title="Permalink to this definition">¶</a></dt>
<dd><p>Enum to specifiy operating precision for engine execution</p>
<p>Members:</p>
<blockquote>
<div><p>float : 32 bit floating point number</p>
<p>float32 : 32 bit floating point number</p>
<p>half : 16 bit floating point number</p>
<p>float16 : 16 bit floating point number</p>
<p>int8 : 8 bit integer number</p>
<p>int32 : 32 bit integer number</p>
<p>bool : Boolean value</p>
<p>unknown : Unknown data type</p>
</div></blockquote>
</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="torch_tensorrt.DeviceType">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">DeviceType</span></span><a class="headerlink" href="#torch_tensorrt.DeviceType" title="Permalink to this definition">¶</a></dt>
<dd><p>Enum to specify device kinds to build TensorRT engines for</p>
<p>Members:</p>
<blockquote>
<div><p>GPU : Specify using GPU to execute TensorRT Engine</p>
<p>DLA : Specify using DLA to execute TensorRT Engine (Jetson Only)</p>
</div></blockquote>
</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="torch_tensorrt.EngineCapability">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">EngineCapability</span></span><a class="headerlink" href="#torch_tensorrt.EngineCapability" title="Permalink to this definition">¶</a></dt>
<dd><p>Enum to specify engine capability settings (selections of kernels to meet safety requirements)</p>
<p>Members:</p>
<blockquote>
<div><p>safe_gpu : Use safety GPU kernels only</p>
<p>safe_dla : Use safety DLA kernels only</p>
<p>default : Use default behavior</p>
</div></blockquote>
</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="torch_tensorrt.TensorFormat">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">TensorFormat</span></span><a class="headerlink" href="#torch_tensorrt.TensorFormat" title="Permalink to this definition">¶</a></dt>
<dd><p>Enum to specifiy the memory layout of tensors</p>
<p>Members:</p>
<blockquote>
<div><p>contiguous : Contiguous memory layout (NCHW / Linear)</p>
<p>channels_last : Channels last memory layout (NHWC)</p>
</div></blockquote>
</dd></dl>

</section>
<section id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="logging.html">torch_tensorrt.logging</a></li>
<li class="toctree-l1"><a class="reference internal" href="ptq.html">torch_tensorrt.ptq</a></li>
<li class="toctree-l1"><a class="reference internal" href="ts.html">torch_tensorrt.ts</a></li>
<li class="toctree-l1"><a class="reference internal" href="fx.html">torch_tensorrt.fx</a></li>
</ul>
</div>
</section>
</section>


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